To reconstruct a 3D point cloud of the roman vessel we use the photogrammetry software COLMAP [1]}.
The output are the camera extrinsics, intrinsics, and a point cloud with 53 million points.
Using this initial reconstruction we train our rendering pipeline to synthesize an HDR image of the scene, which is then tone-mapped to one of the ground truth images.
The exposure value used by the tonemapper is set to the real EV from the EXIF meta data and kept constant during training.
To allow a high radiance variation inside the texture, we store it logarithmically and fit it using the ADAM optimizer [2]}.
The learning rate of the logarithmic texture is reduced by a factor of 10 compared to the linear texture.
All other optimization settings are identical to the previous experiment (Section REF ).
We have trained the system for 300 epochs on half resolution.
This takes around 12 hours on a single GPU workstation equipped with a NVidia Titan V.
